#data_structures

[creativeone333]

Mastering Data Structures in Python: Your Complete Guide

The best way to get ready for questions about data structures in Python is to review basic theoretical ideas and practice solving problems.

[codezclub]

Write a C Program to Implement a Queue using an Array

Write a C Program to Implement a Queue using an Array

#include #define MAX 50 int queue_array[MAX]; int rear = - 1; int front = - 1; main() { int choice; while (1) { printf("1.Insert element to queue \n"); printf("2.Delete element from queue \n"); printf("3.Display all elements of queue \n"); printf("4.Quit \n"); printf("Enter your choice : "); scanf("%d", &choice); switch (choice) { case 1:…

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[codezclub]

Write a C Program to implement Stack Operations Using Arrays

Write a C Program to implement Stack Operations Using Arrays

Write a C Program to implement Stack Operations Using Arrays

  #include #include #include #define size 5 struct stack { int s[size]; int top; } st; int stfull() { if (st.top >= size - 1) return 1; else return 0; } void push(int item) { st.top++; st.s[st.top] = item; } int stempty() { if (st.top == -1) return 1; else return 0; } int pop() { int item; item = st.s[st.top]; st.top--; return (item); }…

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[waxing--artistic--statistic]

Attributes

Attributes and Names

all objects can store attributes or metadata about the object

attributes are essentially named lists

modifying a vector, is a reductive process, much information is lost

however only 3 attributes survive modification

1. names 2. dimensions 3. class

examining/recalling attributes:

attr() for individual recall

attributes() to recall the list at once

assigning attributes

use names(), dim(), and class() not attr()

______________________________________________________________

Names

there are 5 ways to name a vector

(1) during assignment  |  x<-c(a=1,b=2,c-3)

(2) Names() modify existing vector  | x<-1:3, names(x)<-c(’a’,’b’,’c’)

(3) setNames create a modified copy of existing vector  | x<-1:3, x<-setNames(1:3,c(’a’,’b’,’c’))

(4) colnames(matrix)<- c(’name1′,’name2′) or rownames(x)<-

(5) rename a named element with names(object)[indexnumber]<-c(’new name’)

names() to recall a name

if names missing, will return empty “ “ in place of missing elements

if no names, will return NULL

unname() or names(x)<- NULL to create a new vector without names or names(x)<-NULL to remove all names from obj

[fuckyeahpapers]

Keir Fraser

University of Cambridge Technical Report 579

Mutual-exclusion locks are currently the most popular mechanism for interprocess synchronisation, largely due to their apparent simplicity and ease of implementation. In the parallel-computing environments that are increasingly commonplace in high-performance applications, this simplicity is deceptive: mutual exclusion does not scale well with large numbers of locks and many concurrent threads of execution. Highly-concurrent access to shared data demands a sophisticated ‘fine-grained’ locking strategy to avoid serialising non-conflicting operations. Such strategies are hard to design correctly and with good performance because they can harbour problems such as deadlock, priority inversion and convoying. Lock manipulations may also degrade the performance of cache-coherent multiprocessor systems by causing coherency conflicts and increased interconnect traffic, even when the lock protects read-only data.

In looking for solutions to these problems, interest has developed in lock-free data structures. By eschewing mutual exclusion it is hoped that more efficient and robust systems can be built. Unfortunately the current reality is that most lock-free algorithms are complex, slow and impractical. In this dissertation I address these concerns by introducing and evaluating practical abstractions and data structures that facilitate the development of large-scale lock-free systems.

[fuckyeahpapers]

James R. Driscoll , Neil Sarnak , Daniel D. Sleator , Robert E. Tarjan

Journal of Computer And System Sciences

This paper is a study of persistence in data structures. Ordinary data structures are ephemeral in the sense that a change to the structure destroys the old version, leaving only the new version available for use. In contrast, a persistent structure allows access to any version, old or new, at any time. We develop simple, systematic, and efficient techniques for making linked data structures persistent. We use our techniques to devise persistent forms of binary search trees with logarithmic access, insertion, and deletion times and O(1) space bounds for insertion and deletion.